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This study aims to determine and analyze the impact of the work environment on employee job satisfaction at the Human Resources Development Agency (BPSDM) of Jambi Province. The research is motivated by the importance of a supportive work environment in creating comfort, increasing motivation, and improving employee job satisfaction, since preliminary observations found several work-environment problems such as inadequate room cooling, disrupted toilet water supply, and unstable Wi-Fi network. This study used a quantitative approach with an associative research design. The population consisted of 75 employees, all of whom were used as the sample through a saturated sampling technique. Data were collected using a four-point Likert-scale questionnaire and analyzed with validity tests, reliability tests, classical assumption tests, and simple linear regression using SPSS. The results show that the work environment has a significant impact on employee job satisfaction, evidenced by a significance value smaller than 0.05, with a coefficient of determination of 61.8 percent. This indicates that the better the work environment, both physical and non-physical, the higher the employee job satisfaction. This research is expected to serve as a consideration for the agency in improving the quality of the work environment to support employee job satisfaction and performance.

Image Quality Assessment has become an important research area in computer vision due to the increasing use of digital imaging in medical, industrial, and remote sensing applications. This study conducted a Systematic Literature Review (SLR) using articles retrieved exclusively from the Scopus database. A total of fifteen Scopus-indexed articles were selected through the PRISMA process consisting of identification, screening, eligibility, and inclusion stages. The findings indicated that convolutional neural networks, transformer architectures, and no-reference approaches dominated recent studies because of their ability to capture complex visual features and improve evaluation accuracy. The application domain strongly influenced method selection, particularly in medical imaging, industrial quality control, and remote sensing. Deep learning methods achieved high performance but required large datasets and high computational resources. Overall, recent research trends shifted from traditional image enhancement toward artificial intelligence-based quality evaluation.

This study aims to synthesize empirical and theoretical literature on the Planning–Organizing–Actuating–Controlling (POAC) framework in public sector management, identify asymmetries among its functions, and propose a reconfiguration of POAC that is relevant to digital and collaborative governance. The study employs an integrative literature review by examining classical management literature, peer-reviewed journals, government regulations, and official governance indicators. Data were analyzed thematically based on the four POAC functions and synthesized across themes, using Indonesia during the 2021–2025 period as the empirical context. The findings reveal that planning and organizing functions have developed relatively well, while actuating and especially controlling remain persistent weaknesses. This condition is reflected in improvements in several formal governance indicators, including the Electronic-Based Government System (SPBE) Index, Indonesia’s ranking in the E-Government Development Index (EGDI), Unqualified Audit Opinions (WTP), and public service compliance ratings. However, during the same period, the Corruption Perceptions Index (CPI) declined. These findings indicate a gap between administrative achievements and substantive outcomes, consistent with the concept of means–ends decoupling in neo-institutional theory. The study contributes by bridging classical management theory with contemporary governance paradigms and proposing a Data-driven, Networked, Adaptive, and Participatory (DNAP) model of POAC. Practically, the results highlight the need to strengthen controlling functions and adaptive leadership to foster more effective, transparent, and outcome-oriented public governance.

This community service program aims to enhance farmers’ literacy on independent fertilizer production and strengthen their understanding of farmers’ rights within national fertilizer governance as an effort to achieve food sovereignty. The program was implemented in Belang Village, Bungkal Sub-district, Ponorogo Regency, involving GAPOKTAN Baungan consisting of farmer groups from Tempel, Kepuh, Pakal, Kanigoro, and Klatakan hamlets. This initiative represents cross-university synergy involving lecturers from Universitas Merdeka Malang, Universitas Soerjo Ngawi, Universitas Muhammadiyah Ponorogo, and Akademi Kesejahteraan Sosial Ibu Kartini Semarang. The implementation adopted a participatory hybrid approach combining Zoom Meeting dissemination and offline field activities. Activities included literacy enhancement on independent fertilizer production, training on organic fertilizer processing, strengthening farmer group institutions, and legal literacy regarding farmers’ rights in accessing subsidized fertilizers, including RDKK mechanisms and national policies. Methods comprised participatory socialization, hands-on training, group discussions, field practice, and monitoring and evaluation. Results show improved understanding of independent fertilizer concepts, increased technical skills in organic fertilizer production, and greater awareness of farmers’ rights and obligations in the fertilizer distribution system. The program strengthened inter-university collaboration and farmer networks, supporting sustainability, farmer independence, reduced dependence on chemical fertilizers, and community-based food sovereignty.

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

This study examines the gap between Nestlé’s sustainability commitments and the reality of ecological damage caused by the use of unsustainable raw materials in its global supply chain. Using a descriptive qualitative approach based on literature reviews and case study analysis, this study evaluates various data sources, ranging from scientific journals and corporate reports to documentation from independent organizations such as Greenpeace and the Rainforest Action Network. Findings indicate that Nestlé’s procurement of key commodities such as palm oil, cocoa, soy, dairy products, and singleuse plastics significantly contributes to deforestation, systemic plastic pollution, water extraction in vulnerable regions, and greenhouse gas emissions exceeding 87.5 million tons of CO₂e per year. In Indonesia, these highrisk areas are evident in palm oil sourcing in Sumatra and Kalimantan and cocoa sourcing in Sulawesi. Theoretically, this study argues that Nestlé’s sustainability governance exhibits a pattern of strategic decoupling, where public reporting on progress in primary supply chains is deliberately used to mask ongoing environmental damage within their hidden supply networks. Thus, this article makes a critical contribution by integrating supply chain management theory, environmental accountability, and the governance of multinational corporations.

This study aims to analyze the implementation of the Government Employees with Work Agreements (PPPK) orientation program at the Jambi Province Human Resources Development Agency (BPSDM). The research focuses on George C. Edward III's 1980 policy implementation theory, which encompasses communication, resources, disposition, and bureaucratic structure. The research method used is descriptive qualitative. Data collection techniques were conducted through observation, interviews, and documentation, with informants determined using a purposive sampling technique. The results of the study indicate that the implementation of the PPPK Orientation Program has run quite well and has been able to improve participants' understanding of the duties, functions, and core values ​​of the State Civil Apparatus (ASN). However, several obstacles remain in its implementation, namely internet and electricity network constraints, the lack of effective online learning, lack of participant discipline during the activity, and limited supervision and number of committee members. These obstacles affect the optimization of the learning process and the effectiveness of the online orientation activities. Therefore, efforts are needed to improve the quality of program implementation through the development of more interactive learning methods, the provision of adequate facilities, and further evaluation and mentoring to improve the competence and professionalism of ASN.

The development of digital services by BPJS Ketenagakerjaan through the JMO (Jamsostek Mobile) application has triggered a surge in large-scale and unstructured user reviews on the Google Play Store, thereby complicating manual analysis and conventional sentiment analysis in accurately identifying specific issues. This research aims to implement the Aspect-Based Sentiment Analysis (ABSA) method to granularly evaluate JMO application reviews based on specific aspects, while simultaneously addressing class imbalance and computational efficiency issues. The proposed method combines the pretrained IndoBERT model as a contextual feature extractor, the SMOTE technique to balance the training data, and an artificial neural network (Neural Network) as the classification layer without performing full fine-tuning. The dataset used consists of 90,268 unique reviews categorized into five main aspects through keyword matching, namely General Satisfaction/Complaints, Performance & Stability, Service & Support, Feature Quality, and UI/UX, with initial lexicon-based labeling using the InSet Lexicon. The research results indicate that the proposed model successfully achieves highly optimal performance with an accuracy rate of 91.81% and a weighted F1-score of 92%. Furthermore, the implementation of SMOTE proved effective in enhancing model reliability on the minority class (negative sentiment), achieving an F1-score of 89%. The implications of this research contribute an accurate and efficient aspect-based sentiment analysis framework for developers, and serve as a strategic evaluation tool for BPJS Ketenagakerjaan in mapping specific user complaints to accelerate continuous improvements in the performance, stability, and service quality of the JMO application.

Project scheduling is a critical aspect of ensuring the successful implementation of construction projects, particularly bridge projects that involve a high degree of complexity. Poor schedule quality may result in project delays, inaccurate critical path identification, and inadequate project control. This study aims to evaluate the schedule quality of a pile slab bridge project using the Defense Contract Management Agency (DCMA) 14-Point Schedule Assessment method, supported by Oracle Primavera P6 software. The study employed a quantitative descriptive method with a case study approach applied to an existing baseline schedule. The assessment results indicate that several parameters have satisfied the DCMA standards, including relationship type, hard constraints, soft constraints, negative float, large duration, and invalid progress. Conversely, several parameters remain non-compliant with the established standards, namely network logic, positive lag, large float, resources/cost, and the Baseline Execution Index (BEI). This study concludes that the project schedule has not fully adhered to the principles of a network-driven schedule and continues to function primarily as an administrative time schedule. The findings of this study offer practical contributions to construction project management by providing recommendations that encompass the refinement of network logic relationships, reduction of excessive lag usage, implementation of resource and cost loading, and progress updates based on the established baseline, thereby enabling the schedule to serve as a more effective, reliable, and integrated project control tool

Kampung Batik Okra, located in Kranggan Urban Village, Bubutan District, Surabaya City, represents a community-based creative economy initiative officially inaugurated on 28 December 2022 by the Surabaya City Government. This study aims to analyze the facilitating role of the urban village administration in strengthening the creative economic capacity of residents through the batik industry, as well as to identify supporting and inhibiting factors for community empowerment in Kampung Batik Okra. A descriptive qualitative approach was employed, with data collected through in-depth interviews, participatory observation, and document analysis. Informants were purposively selected, encompassing village government officials, batik group administrators, artisans, and academic facilitators. The findings reveal that the urban village administration performs three principal facilitative roles: (1) facilitation of training and skill enhancement; (2) facilitation of market access and promotion; and (3) facilitation of multi-stakeholder partnerships. These roles contribute significantly to the strengthening of residents’ creative economic capacity, as evidenced by an increase in active artisans, product diversification, and expanded marketing networks. Nevertheless, limited human resource capacity within the village administration, inconsistent community participation, and restricted access to capital remain structural barriers requiring more systemic policy interventions. This research offers a conceptual framework of empowerment-based facilitating roles for urban villages, which may serve as a reference for developing creative economy villages in Indonesian urban areas.

Evacuation planning in spatial networks requires the identification of critical nodes that maintain connectivity, accessibility, and flow distribution during emergency situations. Existing approaches often rely on individual centrality measures, which capture only a single structural dimension of node importance and may therefore produce incomplete or biased prioritization. To address this limitation, this study proposes a Composite Centrality Framework for identifying critical nodes in meso-scale spatial networks with semi-structured connectivity. The network is modeled as a weighted undirected graph, and Degree, Betweenness, and Closeness Centrality are integrated into a unified composite index to capture complementary structural roles. The framework is implemented in MATLAB and evaluated using a real-world campus spatial network consisting of 30 nodes and a synthetic network comprising 16 nodes with comparable structural characteristics. The results reveal a highly uneven distribution of node importance, with a small set of structurally dominant nodes consistently identified across both networks. In the campus network, node P1 achieves the highest composite centrality score (0.2195) and ranks first across the individual centrality measures, indicating its dominant role in maintaining network connectivity, accessibility, and flow distribution. Quantitative evaluation demonstrates strong agreement between the composite ranking and the individual measures, with Spearman rank correlation coefficients of 0.94, 0.89, and 0.91 for Degree, Betweenness, and Closeness Centrality, respectively. However, only one node (P1) appears simultaneously in the top five of all rankings, highlighting the complementary nature of the individual centrality measures and supporting the need for multi-criteria integration. Sensitivity analysis across three weighting scenarios yields rank correlations exceeding 0.97, confirming ranking stability and methodological robustness. Overall, the proposed framework provides a balanced and reliable approach for identifying critical nodes and demonstrates potential applicability to evacuation planning and spatial network analysis in semi-structured environments.

Urban poverty remains a complex social problem that affects various community groups, including scavengers who work within the informal sector. This study aims to analyze the survival strategies of scavengers in Tangerang City and reflect on their experiences from a Christian theological perspective. The research uses a qualitative approach through literature study and descriptive analysis of relevant social phenomena. The findings show that scavengers develop several survival strategies, including utilizing family and community networks, working persistently, selecting strategic collection areas, managing limited income, and adapting to uncertain environmental and economic conditions. Nevertheless, scavengers continue to face social marginalization, economic vulnerability, occupational risks, limited access to health services, and inadequate social protection. From a Christian theological perspective, this reality calls for a response grounded in love, justice, human dignity, and concern for marginalized communities. Therefore, churches, local communities, and wider society are expected to participate actively in empowering scavengers through holistic, inclusive, and contextual approaches that strengthen welfare, social recognition, and sustainable livelihoods.

The digitalization of tax administration has become one of the Indonesian government's strategic initiatives to improve efficiency, transparency, and taxpayer compliance. However, the implementation of digital tax systems among Micro, Small, and Medium Enterprises (MSMEs), particularly distributor-sector MSMEs in Batam City, still faces various administrative and technical challenges. This study aims to examine the experiences, risk perceptions, and adaptation strategies of distributor MSME actors in responding to the digitalization of tax administration following the implementation of the Harmonization of Tax Regulations Law (UU HPP). The research employed a qualitative approach using a phenomenological method. Data were collected through in-depth interviews with seven distributor MSME owners in Batam City who had utilized digital tax systems such as e-Filing and Coretax. The findings indicate that most business actors still experience limitations in technical understanding, concerns regarding reporting errors, and difficulties adapting to changes in digital tax systems and regulations. To address these challenges, MSME actors developed several adaptation strategies, including the use of tax consultants, hybrid manual-digital bookkeeping systems, and informal assistance through the internet and business networks. This study highlights that the success of tax digitalization requires a more adaptive approach, stable systems, and policies that are aligned with the characteristics of local MSMEs.

This study aims to analyze the dynamics of urban growth, land use change, and settlement development in Wamena City during 2020–2025. The research employed a quantitative descriptive approach using satellite imagery and population data obtained from the Central Bureau of Statistics (BPS) of Jayawijaya Regency. The analysis was conducted through satellite image interpretation to identify land use changes and the expansion of built-up areas.The results indicate that the built-up area in Wamena City increased from 1,250 hectares in 2020 to 2,012 hectares in 2025. Meanwhile, the population increased from 44,315 people to 49,102 people during the same period. Land use changes were dominated by the conversion of open land into residential areas and other urban facilities. Settlement growth generally followed the main road networks, city center, and areas surrounding Wamena Airport. Population growth, urbanization, economic activities, and infrastructure development were identified as the main factors influencing urban growth dynamics in Wamena City. These conditions resulted in reduced open spaces and increased urban density.

The high volatility of cryptocurrency assets, particularly Ethereum, poses a significant challenge for investors during market downturns. Traditional passive strategies often lead to substantial capital erosion in bearish conditions. This study explores the application of Deep Reinforcement Learning (DRL) through the Deep Q-Network (DQN) algorithm to develop an adaptive trading agent. By integrating technical indicators—Relative Strength Index (RSI), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD)—the proposed model aims to optimize decision-making processes. Experimental results using historical data from 2020 to 2026 demonstrate that while the market experienced a significant decline of 19.55%, the DQN agent successfully maintained capital stability with a marginal deviation of only -0.54%. This finding suggests that the DQN-based approach offers superior risk mitigation and capital preservation capabilities compared to conventional buy-and-hold strategies in volatile financial environments.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

This study aims to analyze the implementation of good governance principles in digital population administration services in Medan Marelan Subdistrict from the perspective of Administrative Law. The background of this research is based on the importance of digital transformation in public services through the Electronic-Based Government System (SPBE) to enhance transparency, accountability, effectiveness, and efficiency. However, in practice, several obstacles are still encountered, such as limited infrastructure, network disruptions, and limited human resources. This research employs an empirical legal research method with both empirical and statutory approaches. Data were collected through field research, including interviews with subdistrict officials and the community, as well as library research involving laws, regulations, and relevant literature. The data were analyzed qualitatively to examine the conformity between the implementation of services and the principles of good governance and general principles of good administration (AUPB). The results show that the implementation of good governance principles in digital population services in Medan Marelan Subdistrict has not been fully optimal. This is indicated by challenges in information transparency, system effectiveness, and limitations in human resources and infrastructure. Nevertheless, digital services have provided convenience for the community in accessing population administration services. In conclusion, improvements in human resources quality, technological infrastructure, and the strengthening of supervision and evaluation mechanisms are necessary to achieve public services that align with good governance principles.

Amidst rising global pressure regarding sustainability and cross-border trade, customs agencies are no longer viewed merely as gatekeepers but as strategic actors in global environmental governance through the Green Customs Initiative (GCI). This research aims to uncover the evolutionary patterns of GCI research within the context of adaptive policy, map its intellectual structure, and identify future thematic directions. The methodology employs a bibliometric analysis of documents sourced from the Web of Science database, utilizing VOSviewer software to perform co-authorship, bibliographic coupling, and co-word analysis.The results demonstrate that the dominance of scientific actors in this field is determined more by the strength of collaboration networks than by the mere volume of publications. The findings identify that the primary intellectual foundations of GCI research rest upon the critical relationship between environmental regulation, international trade, market responses, and eco-innovation. Furthermore, country-level analysis highlights the central roles of China, the United States, and the United Kingdom in shaping the knowledge structure of this domain. Overall, these findings underscore that the GCI has evolved into a multidisciplinary field that requires an adaptive policy framework to respond to the dynamics of ecological risks and global trade demands through institutional flexibility and cross-actor collaboration. By integrating environmental protection into border management and risk assessment, this study provides a strategic reference for customs authorities to move toward a more responsive and knowledge-based green customs administration.

Post-disaster settlement recovery requires more than the provision of permanent housing; it demands an integrated approach covering social, economic, institutional, environmental, and disaster risk reduction dimensions. This study analyzes the Settlement Planning Document (RPP) of the Sibalaya Selatan Satellite Permanent Resettlement Area (Huntap) in Sigi Regency, Central Sulawesi, which accommodates 118 households displaced by the 2018 Palu liquefaction disaster. The research examines the RPP as a participatory planning instrument for sustainable post-disaster recovery. A qualitative participatory approach using the Community Action Plan (CAP) method was conducted from July to November 2024 through focus group discussions, participatory workshops, formation of a Core Planning Team (TIP), field observations, document reviews, and public consultations. Data were analyzed using descriptive, infrastructure, spatial, and strengths-problems-opportunities-challenges frameworks. The findings reveal that community social capital, reflected in active organizations, social networks, and digital communication infrastructure, has been transformed into economic capital. In addition, four dimensions of community resilience economic, social, communication, and competence capacities were identified at varying levels of development. The TIP also played a strategic role in integrating the RPP into village and regional development planning. The resulting RPP contains 39 priority programs valued at approximately IDR 340.6 million for 2024-2028 across infrastructure, economic, social-environmental, and disaster risk reduction sectors. The study demonstrates that the CAP approach effectively supports sustainable settlement development and offers a replicable model for post-disaster resettlement areas in Indonesia.

Magelang City faces the challenge of limited land availability, with a total area of only 18.58 km2 and a high population density. Consequently, telecommunications infrastructure development requires a precise strategy. This study aims to analyze the business model and investment feasibility of Pole and Greenfield type telecommunication towers in Magelang City. Using a descriptive quantitative approach, this research processes secondary data from PT Dayamitra Telekomunikasi Indonesia by applying feasibility analysis based on Life Cycle Costing (LCC), Net Present Value (NPV), Internal Rate of Return (IRR), Break-Even Point (BEP), Payback Period (PP), and Benefit-Cost Ratio (BCR). The results indicate significant differences in cost structures; Pole towers proved to be more efficient, requiring an initial capital outlay of only 28.8% of the total capital required for Greenfield towers. Greenfield towers generated an NPV of Rp13.07 billion with an IRR of 20%, while Pole towers generated an NPV of Rp2.46 billion with a higher IRR of 23%. Pole towers have proven to offer a faster return on investment and better operational cost efficiency, making them the most strategic option to support network densification and the implementation of 5G technology in urban areas with spatial constraints like Magelang City.